






"""simple docstring"""
import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import DetrImageProcessor



class        UpperCamelCase__ (				unittest.TestCase):







    """simple docstring"""







    def __init__(		self :	str				,    UpperCamelCase_ :	Dict				,    UpperCamelCase_ :	int=7				,    UpperCamelCase_ :	Dict=3				,    UpperCamelCase_ :	Dict=3_0				,    UpperCamelCase_ :	List[str]=4_0_0				,    UpperCamelCase_ :	Tuple=True				,    UpperCamelCase_ :	Any=None				,    UpperCamelCase_ :	Optional[Any]=True				,    UpperCamelCase_ :	str=1 / 2_5_5				,    UpperCamelCase_ :	int=True				,    UpperCamelCase_ :	Optional[Any]=[0.5, 0.5, 0.5]				,    UpperCamelCase_ :	Union[str, Any]=[0.5, 0.5, 0.5]				,    UpperCamelCase_ :	Any=True				,    ):





           '''simple docstring'''

           __magic_name__      				=    size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
           __magic_name__      				=    parent
           __magic_name__      				=    batch_size
           __magic_name__      				=    num_channels
           __magic_name__      				=    min_resolution
           __magic_name__      				=    max_resolution
           __magic_name__      				=    do_resize
           __magic_name__      				=    size
           __magic_name__      				=    do_rescale
           __magic_name__      				=    rescale_factor
           __magic_name__      				=    do_normalize
           __magic_name__      				=    image_mean
           __magic_name__      				=    image_std
           __magic_name__      				=    do_pad







    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_rescale": self.do_rescale,
               "rescale_factor": self.rescale_factor,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_pad": self.do_pad,
           }







    def 				a__			(		self :	str				,    UpperCamelCase_ :	Dict				,    UpperCamelCase_ :	Union[str, Any]=False ):





           '''simple docstring'''

           if not batched:
                  __magic_name__      				=    image_inputs[0]
                  if isinstance(UpperCamelCase_				,    Image.Image ):
                         __magic_name__				,  __magic_name__      				=    image.size
                  else:
                         __magic_name__				,  __magic_name__      				=    image.shape[1], image.shape[2]
                  if w < h:
                         __magic_name__      				=    int(self.size['shortest_edge'] * h / w )
                         __magic_name__      				=    self.size['shortest_edge']
                  elif w > h:
                         __magic_name__      				=    self.size['shortest_edge']
                         __magic_name__      				=    int(self.size['shortest_edge'] * w / h )
                  else:
                         __magic_name__      				=    self.size['shortest_edge']
                         __magic_name__      				=    self.size['shortest_edge']

           else:
                  __magic_name__      				=    []
                  for image in image_inputs:
                         __magic_name__				,  __magic_name__      				=    self.get_expected_values([image] )
                         expected_values.append((expected_height, expected_width) )
                  __magic_name__      				=    max(UpperCamelCase_				,    key=lambda UpperCamelCase_ : item[0] )[0]
                  __magic_name__      				=    max(UpperCamelCase_				,    key=lambda UpperCamelCase_ : item[1] )[1]

           return expected_height, expected_width



@require_torch
@require_vision
class        UpperCamelCase__ (				a_							,	unittest.TestCase):







    """simple docstring"""
    __UpperCAmelCase           = DetrImageProcessor if is_vision_available() else None







    def 				a__			(		self :	Union[str, Any] ):





           '''simple docstring'''

           __magic_name__      				=    DetrImageProcessingTester(self )







    @property
    def 				a__			(		self :	Optional[Any] ):





           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()







    def 				a__			(		self :	Dict ):





           '''simple docstring'''

           __magic_name__      				=    self.image_processing_class(**self.image_processor_dict )
           self.assertTrue(hasattr(UpperCamelCase_				,    'image_mean' ) )
           self.assertTrue(hasattr(UpperCamelCase_				,    'image_std' ) )
           self.assertTrue(hasattr(UpperCamelCase_				,    'do_normalize' ) )
           self.assertTrue(hasattr(UpperCamelCase_				,    'do_rescale' ) )
           self.assertTrue(hasattr(UpperCamelCase_				,    'rescale_factor' ) )
           self.assertTrue(hasattr(UpperCamelCase_				,    'do_resize' ) )
           self.assertTrue(hasattr(UpperCamelCase_				,    'size' ) )
           self.assertTrue(hasattr(UpperCamelCase_				,    'do_pad' ) )







    def 				a__			(		self :	Union[str, Any] ):





           '''simple docstring'''

           __magic_name__      				=    self.image_processing_class.from_dict(self.image_processor_dict )
           self.assertEqual(image_processor.size				,    {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3} )
           self.assertEqual(image_processor.do_pad				,    UpperCamelCase_ )

           __magic_name__      				=    self.image_processing_class.from_dict(
               self.image_processor_dict				,    size=4_2				,    max_size=8_4				,    pad_and_return_pixel_mask=UpperCamelCase_ )
           self.assertEqual(image_processor.size				,    {'shortest_edge': 4_2, 'longest_edge': 8_4} )
           self.assertEqual(image_processor.do_pad				,    UpperCamelCase_ )







    def 				a__			(		self :	Dict ):





           '''simple docstring'''

           pass







    def 				a__			(		self :	Union[str, Any] ):





           '''simple docstring'''

           __magic_name__      				=    self.image_processing_class(**self.image_processor_dict )
           # create random PIL images
           __magic_name__      				=    prepare_image_inputs(self.image_processor_tester				,    equal_resolution=UpperCamelCase_ )
           for image in image_inputs:
                  self.assertIsInstance(UpperCamelCase_				,    Image.Image )

           # Test not batched input
           __magic_name__      				=    image_processing(image_inputs[0]				,    return_tensors='pt' ).pixel_values

           __magic_name__				,  __magic_name__      				=    self.image_processor_tester.get_expected_values(UpperCamelCase_ )

           self.assertEqual(
               encoded_images.shape				,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,    )

           # Test batched
           __magic_name__				,  __magic_name__      				=    self.image_processor_tester.get_expected_values(UpperCamelCase_				,    batched=UpperCamelCase_ )

           __magic_name__      				=    image_processing(UpperCamelCase_				,    return_tensors='pt' ).pixel_values
           self.assertEqual(
               encoded_images.shape				,    (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )				,    )







    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           __magic_name__      				=    self.image_processing_class(**self.image_processor_dict )
           # create random numpy tensors
           __magic_name__      				=    prepare_image_inputs(self.image_processor_tester				,    equal_resolution=UpperCamelCase_				,    numpify=UpperCamelCase_ )
           for image in image_inputs:
                  self.assertIsInstance(UpperCamelCase_				,    np.ndarray )

           # Test not batched input
           __magic_name__      				=    image_processing(image_inputs[0]				,    return_tensors='pt' ).pixel_values

           __magic_name__				,  __magic_name__      				=    self.image_processor_tester.get_expected_values(UpperCamelCase_ )

           self.assertEqual(
               encoded_images.shape				,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,    )

           # Test batched
           __magic_name__      				=    image_processing(UpperCamelCase_				,    return_tensors='pt' ).pixel_values

           __magic_name__				,  __magic_name__      				=    self.image_processor_tester.get_expected_values(UpperCamelCase_				,    batched=UpperCamelCase_ )

           self.assertEqual(
               encoded_images.shape				,    (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )				,    )







    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           __magic_name__      				=    self.image_processing_class(**self.image_processor_dict )
           # create random PyTorch tensors
           __magic_name__      				=    prepare_image_inputs(self.image_processor_tester				,    equal_resolution=UpperCamelCase_				,    torchify=UpperCamelCase_ )
           for image in image_inputs:
                  self.assertIsInstance(UpperCamelCase_				,    torch.Tensor )

           # Test not batched input
           __magic_name__      				=    image_processing(image_inputs[0]				,    return_tensors='pt' ).pixel_values

           __magic_name__				,  __magic_name__      				=    self.image_processor_tester.get_expected_values(UpperCamelCase_ )

           self.assertEqual(
               encoded_images.shape				,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,    )

           # Test batched
           __magic_name__      				=    image_processing(UpperCamelCase_				,    return_tensors='pt' ).pixel_values

           __magic_name__				,  __magic_name__      				=    self.image_processor_tester.get_expected_values(UpperCamelCase_				,    batched=UpperCamelCase_ )

           self.assertEqual(
               encoded_images.shape				,    (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )				,    )







    @slow
    def 				a__			(		self :	Any ):





           '''simple docstring'''

           __magic_name__      				=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
           with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'				,    'r' ) as f:
                  __magic_name__      				=    json.loads(f.read() )

           __magic_name__      				=    {'image_id': 3_9_7_6_9, 'annotations': target}

           # encode them
           __magic_name__      				=    DetrImageProcessor.from_pretrained('facebook/detr-resnet-50' )
           __magic_name__      				=    image_processing(images=UpperCamelCase_				,    annotations=UpperCamelCase_				,    return_tensors='pt' )

           # verify pixel values
           __magic_name__      				=    torch.Size([1, 3, 8_0_0, 1_0_6_6] )
           self.assertEqual(encoding['pixel_values'].shape				,    UpperCamelCase_ )

           __magic_name__      				=    torch.tensor([0.2_796, 0.3_138, 0.3_481] )
           self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]				,    UpperCamelCase_				,    atol=1e-4 ) )

           # verify area
           __magic_name__      				=    torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438] )
           self.assertTrue(torch.allclose(encoding['labels'][0]['area']				,    UpperCamelCase_ ) )
           # verify boxes
           __magic_name__      				=    torch.Size([6, 4] )
           self.assertEqual(encoding['labels'][0]['boxes'].shape				,    UpperCamelCase_ )
           __magic_name__      				=    torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215] )
           self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]				,    UpperCamelCase_				,    atol=1e-3 ) )
           # verify image_id
           __magic_name__      				=    torch.tensor([3_9_7_6_9] )
           self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']				,    UpperCamelCase_ ) )
           # verify is_crowd
           __magic_name__      				=    torch.tensor([0, 0, 0, 0, 0, 0] )
           self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']				,    UpperCamelCase_ ) )
           # verify class_labels
           __magic_name__      				=    torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
           self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']				,    UpperCamelCase_ ) )
           # verify orig_size
           __magic_name__      				=    torch.tensor([4_8_0, 6_4_0] )
           self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']				,    UpperCamelCase_ ) )
           # verify size
           __magic_name__      				=    torch.tensor([8_0_0, 1_0_6_6] )
           self.assertTrue(torch.allclose(encoding['labels'][0]['size']				,    UpperCamelCase_ ) )







    @slow
    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           __magic_name__      				=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
           with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'				,    'r' ) as f:
                  __magic_name__      				=    json.loads(f.read() )

           __magic_name__      				=    {'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

           __magic_name__      				=    pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic' )

           # encode them
           __magic_name__      				=    DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic' )
           __magic_name__      				=    image_processing(images=UpperCamelCase_				,    annotations=UpperCamelCase_				,    masks_path=UpperCamelCase_				,    return_tensors='pt' )

           # verify pixel values
           __magic_name__      				=    torch.Size([1, 3, 8_0_0, 1_0_6_6] )
           self.assertEqual(encoding['pixel_values'].shape				,    UpperCamelCase_ )

           __magic_name__      				=    torch.tensor([0.2_796, 0.3_138, 0.3_481] )
           self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]				,    UpperCamelCase_				,    atol=1e-4 ) )

           # verify area
           __magic_name__      				=    torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147] )
           self.assertTrue(torch.allclose(encoding['labels'][0]['area']				,    UpperCamelCase_ ) )
           # verify boxes
           __magic_name__      				=    torch.Size([6, 4] )
           self.assertEqual(encoding['labels'][0]['boxes'].shape				,    UpperCamelCase_ )
           __magic_name__      				=    torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625] )
           self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]				,    UpperCamelCase_				,    atol=1e-3 ) )
           # verify image_id
           __magic_name__      				=    torch.tensor([3_9_7_6_9] )
           self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']				,    UpperCamelCase_ ) )
           # verify is_crowd
           __magic_name__      				=    torch.tensor([0, 0, 0, 0, 0, 0] )
           self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']				,    UpperCamelCase_ ) )
           # verify class_labels
           __magic_name__      				=    torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
           self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']				,    UpperCamelCase_ ) )
           # verify masks
           __magic_name__      				=    8_2_2_8_7_3
           self.assertEqual(encoding['labels'][0]['masks'].sum().item()				,    UpperCamelCase_ )
           # verify orig_size
           __magic_name__      				=    torch.tensor([4_8_0, 6_4_0] )
           self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']				,    UpperCamelCase_ ) )
           # verify size
           __magic_name__      				=    torch.tensor([8_0_0, 1_0_6_6] )
           self.assertTrue(torch.allclose(encoding['labels'][0]['size']				,    UpperCamelCase_ ) )







"""simple docstring"""
import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
snake_case : Optional[Any]     		=		"""."""

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
snake_case : List[str]     		=		[
    """Assert""",
    """AssignVariableOp""",
    """EmptyTensorList""",
    """MergeV2Checkpoints""",
    """ReadVariableOp""",
    """ResourceGather""",
    """RestoreV2""",
    """SaveV2""",
    """ShardedFilename""",
    """StatefulPartitionedCall""",
    """StaticRegexFullMatch""",
    """VarHandleOp""",
]


def 							A						(				__snake_case: Optional[Any] ,     __snake_case: Tuple ,     __snake_case: Any  )    ->		int:

       """simple docstring"""





       __magic_name__      				=    SavedModel()
       __magic_name__      				=    []

       with open(os.path.join(__snake_case ,     'utils' ,     'tf_ops' ,     'onnx.json'  )  ) as f:
              __magic_name__      				=    json.load(__snake_case  )['opsets']

       for i in range(1 ,     opset + 1  ):
              onnx_ops.extend(onnx_opsets[str(__snake_case  )]  )

       with open(__snake_case ,     'rb'  ) as f:
              saved_model.ParseFromString(f.read()  )

       __magic_name__      				=    set()

       # Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
       for meta_graph in saved_model.meta_graphs:
              # Add operations in the graph definition
              model_op_names.update(node.op for node in meta_graph.graph_def.node  )

              # Go through the functions in the graph definition
              for func in meta_graph.graph_def.library.function:
                     # Add operations in each function
                     model_op_names.update(node.op for node in func.node_def  )

    # Convert to list, sorted if you want
       __magic_name__      				=    sorted(__snake_case  )
       __magic_name__      				=    []

       for op in model_op_names:
              if op not in onnx_ops and op not in INTERNAL_OPS:
                     incompatible_ops.append(__snake_case  )

       if strict and len(__snake_case  ) > 0:
              raise Exception(F"""Found the following incompatible ops for the opset {opset}:\n""" + incompatible_ops  )
       elif len(__snake_case  ) > 0:
              print(F"""Found the following incompatible ops for the opset {opset}:"""  )
              print(*__snake_case ,     sep='\n'  )
       else:
              print(F"""The saved model {saved_model_path} can properly be converted with ONNX."""  )


if __name__ == "__main__":
 snake_case : Optional[Any]     		=		argparse.ArgumentParser()
 parser.add_argument("""--saved_model_path""", help="""Path of the saved model to check (the .pb file).""")
 parser.add_argument(
     """--opset""", default=1_2, type=int, help="""The ONNX opset against which the model has to be tested."""
 )
 parser.add_argument(
     """--framework""", choices=["""onnx"""], default="""onnx""", help="""Frameworks against which to test the saved model."""
 )
 parser.add_argument(
     """--strict""", action="""store_true""", help="""Whether make the checking strict (raise errors) or not (raise warnings)"""
 )
 snake_case : Any     		=		parser.parse_args()

 if args.framework == "onnx":
  onnx_compliancy(args.saved_model_path, args.strict, args.opset)